统计学系系列讲座之356-358期

时　间：2019年10月18日（星期五）10:30-11:30

地　点：史带楼603室

主持人：郁文  教授 复旦大学管理学院统计学系

主　题：Interval Data: Modeling and Visualization

主讲人：Dennis Kon-Jin Lin,

　　　　University Distinguished Professor

　　　　Department of Statistics, The Pennsylvania State University, USA.

简　介：

Dr. Dennis K. J. Lin is a university distinguished professor of supply chain and statistics at Penn State University. His research interests are quality assurance, industrial statistics, data mining, and response surface. He has published near 200 SCI/SSCI papers in a wide variety of journals. He currently serves or has served as associate editor for more than 10 professional journals and was co-editor for Applied Stochastic Models for Business and Industry. Dr. Lin is an elected fellow of ASA, IMS and ASQ, an elected member of ISI, a lifetime member of ICSA, and a fellow of RSS. He is an honorary chair professor for various universities, including Renmin University of China (as a Chang-Jiang Scholar), Fudan University, and National Chengchi University (Taiwan). His recent awards including, the Youden Address (ASQ, 2010), the Shewell Award (ASQ, 2010), the Don Owen Award (ASA, 2011), the Loutit Address (SSC, 2011), the Hunter Award (ASQ, 2014), and the Shewhart Medal (ASQ, 2015). This yea, he is awarded SPES Award at the 2016 Joint Statistical Meeting.

摘　要：

Interval-valued data is a special symbolic data composed of lower and upper bounds of intervals. It can be generated from the change of climate, fluctuation of stock prices, daily blood pressures, aggregation of large datasets, and many other situations. Such type of data contains rich information useful for decision making. The prediction of interval-valued data is a challenging task as the predicted lower bounds of intervals should not cross over the corresponding upper bounds. In this project, a regularized artificial neural network (RANN) is proposed to address this difficult problem. It provides a flexible trade-off between prediction accuracy and interval crossing. Empirical study indicates the usefulness and accuracy of the proposed method. The second portion of this project provides some new insights for visualization of interval data.  Two plots are proposed—segment plot and dandelion plot.  The new approach compensates the existing visualization methods and provides much more information.  Theorems have been established for reading these new plots.  Examples are given for illustration.